Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (5): 1484-1491.DOI: 10.11772/j.issn.1001-9081.2020081162

Special Issue: 前沿与综合应用

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

High-accuracy localization algorithm based on fusion of two-dimensional code vision and laser lidar

LUAN Jianing1, ZHANG Wei1, SUN Wei2, ZHANG Ao2, HAN Dong2   

  1. 1. College of Electronic and Information Engineering, Tongji University, Shanghai 201804, China;
    2. School of Aerospace Science and Technology, Xidian University, Xi'an Shaanxi 710126, China
  • Received:2020-08-05 Revised:2020-11-13 Online:2021-05-10 Published:2020-11-25
  • Supported by:
    This work is partially supported by the National Key Research and Development Program of China (2017YFC0805004).

基于二维码视觉与激光雷达融合的高精度定位算法

栾佳宁1, 张伟1, 孙伟2, 张奥2, 韩冬2   

  1. 1. 同济大学 电子与信息工程学院, 上海 201804;
    2. 西安电子科技大学 空间科学与技术学院, 西安 710126
  • 通讯作者: 张伟
  • 作者简介:栾佳宁(1997-),男,辽宁大连人,硕士研究生,主要研究方向:移动机器人定位导航;张伟(1975-),男,上海人,副教授,博士,主要研究方向:传感器网络、智能感知;孙伟(1980-),男,安徽宿州人,教授,博士,主要研究方向:无人机集群控制、高性能视觉信息计算;张奥(2000-),男,重庆人,主要研究方向:移动机器人SLAM;韩冬(1997-),男,湖南常德人,硕士研究生,主要研究方向:机器人导航。
  • 基金资助:
    国家重点研发计划项目(2017YFC0805004)。

Abstract: Traditional laser localization algorithms such as Monte Carlo localization algorithm have the problems of low accuracy and poor anti-robot kidnapping performance, and traditional two-dimensional code localization algorithms have complex environmental layout and strict limitation to robot's trajectory. In order to solve these problems, a mobile robot localization algorithm based on two-dimensional code vision and laser lidar data was proposed. Firstly, the computer vision technology was used by the robot to detect two-dimensional codes in the test environment, and the poses of detecting two-dimensional codes were transformed to map coordinates respectively, and they were fused to generate the prior pose information. Then the optimized pose was obtained by the point cloud alignment with the generated information as the initial poses. At the same time, the odometry-vision supervising mechanism was introduced to effectively solve the problems brought by the environmental factors such as the information lack of two-dimensional codes and the wrong recognition of the two-dimensional codes as well as ensure the smoothness of the poses. Finally, experimental results based on mobile robot show that, the proposed algorithm has the average error of lidar sampling points reduced by 92%, the average time spent per pose calculation reduced by 88% compared with the classical Adaptive Monto Carlo Localization (AMCL) algorithm, and it solves robot kidnapping problem effectively. This algorithm can be applied to the indoor robots such as storage robot.

Key words: indoor localization, mobile robot, laser lidar, two-dimensional code, point cloud alignment, sensor fusion

摘要: 为解决以蒙特卡罗定位算法为代表的激光室内定位算法存在的定位精度差和抗机器人绑架性能差的问题,以及传统二维码定位算法环境布置复杂且对机器人运行轨迹有严格要求的问题,提出了一种融合二维码视觉和激光雷达数据的移动机器人定位算法。机器人首先利用机器视觉技术搜索检测环境中的二维码,然后将检测出二维码的位姿分别转换至地图坐标系下,并融合生成先验位姿信息。而后以此作为初始位姿进行点云对准以得到优化后的位姿。同时引入里程计-视觉监督机制,从而有效解决了包括二维码信息缺失、二维码识别错误等由环境因素带来的问题,并保证了位姿的平滑性。基于移动机器人的实验结果表明,所提算法比经典的自适应蒙特卡罗定位(AMCL)算法的雷达采样点平均误差下降了92%,单次位姿计算时间减少了88%,可有效解决机器人绑架问题,并应用于以仓储机器人为代表的室内移动机器人。

关键词: 室内定位, 移动机器人, 激光雷达, 二维码, 点云配准, 传感器融合

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